Neoformed contaminants (NFC) are new molecules formed in a food based on components specific to said food, due to the action of transformation methods, and which present public health problems. Few public or private laboratories are capable today of assaying these contaminants. Moreover, even if numerous methods have been published for the assay of each of the contaminants, none of them has really been the subject of standardization and normalization. In addition, these analyses are costly and the time period for obtaining results is long (2 to 3 weeks). The industry requires faster and less expensive analysis methods to ensure control of conformity of its methods and the safety of the foods that it produces.
Food transformation methods (cooking, sterilization, preservation) can also have an unfavorable influence on the nutritional properties of foods, for example by reducing their vitamin content. The characterization of these methods and their influence on the nutritional properties of foods presents problems similar to those, mentioned above, of assaying neoformed contaminants.
The use of analyses, in particular spectroscopic, of foods using the methods of chemometry, and in particular multi-way analysis, is known.
Multi-way analysis is the natural extension of multivariate analysis when the data is arranged in three-way or more-than-three-way tables. It is based on the use of statistical models such as “PARAFAC” (“Parallel Factor”) and NPLS (“N-ways Partial Least Squares regression”). These methods, as well as their use in the analysis of food products, are described in the document [Bro 1998].
More specifically, [Rizkallah 2007] describes a method for analyzing unprepared samples (only ground, if necessary) based on the use of the PARAFAC model with front-face fluorescence spectra. This method involves the illumination of a sample by a plurality of monochromatic light radiations with respective wavelengths, and the acquisition of corresponding fluorescence spectra. The excitation radiation will finely sample (at several hundred points) a spectral range covering the visible and the near-UV; in turn, the fluorescence emission spectra are spectrally sampled (also at several hundred points). The spectral data thus collected for each sample is arranged in a large matrix called an “excitation-emission matrix” (EEM), of which one dimension represents the emission wavelengths and the other dimension represents the emission wavelengths.
The EEMs corresponding to a plurality of calibration samples are analyzed by the PARAFAC method, which makes it possible to extract information, called “PARAFAC factors”, which correspond to the bi-linear fluorescent profiles and to their relative intensities. Then, a multiple regression between the fluorescent intensities and the neoformed contaminant content (or content of other substances of interest) chemically measured makes it possible to construct a calibration model that is then used to predict the content of neoformed contaminants on the basis of the EEMs collected on new samples to be analyzed.
This method has two major limitations:    First, it requires high-cost equipment: a spectrofluorometer equipped with a Xenon lamp delivering all of the wavelengths from the near-UV to the visible, and two monochromators capable of transmitting these different excitation and emission for the photons emitted by the sample.    Second, its implementation is time-consuming because, even if the preparation of the sample is not necessary, the acquisition of each EEM takes 15 to 45 minutes according to the desired spectral resolution. The analysis of these data matrices, including a considerable number of variables, is also onerous in spite of the automation of data processing algorithms.
A similar method is described by [Rizkallah et al., 2008].
The document [Nahorniak 2003] studies the influence of spectral resolution (and therefore the size of the EEM matrix) on the prediction error of a fluorophore by applying the PARAFAC method. In this document, diluted solutions with a simple and known composition are considered; in addition, all of the measurements are performed with a high spectral resolution, of 2 nm/pixel, and the reduction in resolution is obtained by calculating spectral averages.
This study finds that the prediction error is minimized—and is around 0.5 to 1%—for an excitation resolution of 6 to 10 nm/pixel, then increases significantly for lower resolutions (increase by a factor of 2 to 6, reaching a resolution of 20 nm/pixel). It therefore appears to be difficult to use resolutions below 20 nm/pixel.